What is CNN in deep learning? Convolutional Neural Network Explained

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What is CNN in Deep Learning?

In this video, we understand what is CNN in Deep Learning and why do we need it.

CNN (or Convolutional Neural Network) is the building block of all Computer Vision applications. Applications like self-driving cars, object recognition, face recognition, etc.

There is a limitation of simple Neural Networks when it comes to dealing with images. They become extremely slow at training and processing images. And the number of parameters to train will also be very large.

So, to overcome this limitation, we use Convolutional Neural Network In deep learning.

Watch the video till the end to understand what is CNN in deep learning.

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Timestamp:
0:00 Intro
1:27 Drawback of ANN
3:12 Convolutional Neural Network
5:34 End

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Follow my entire playlist on Convolutional Neural Network (CNN), because I provide a very detailed mathematical explanation about every model, along with practical implementation.

At the end of some videos, you will also find quizzes that can help you to understand the concept and retain your learning.

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Thank you very much bro, amazing explanation

ikromjonovhojiakbar

Awesome content.. Loving your videos..

Kapilwankhede

This is a beautiful picture of mine ! got me 😂

jeyajothi

What does it mean Image datasets coloured and it has coloured with blue green rgb channels

Njoudalnamlah

Wow Bro You explained it sooo Goood, I have a seminar presentation and i was searching youtube for this at last moment, your video helped me a lot Thank you!

fzxsizs

Great explanation! Awesome Video!
Super excited for the CNN series 😁

SahilBondre

Amazing video !!! Keep up the great work

achalcharantimath

3*3px leads to 9 parameters or 3*3*3 = 27 parameters (the trailing 3 is for RGB) ?

harsh_hybrid_thenx

bro if possible share some notes too, it would be helpful

mohithvepa

Could you plzzz teach me cnn from strach .. because after completely watched you videos I have certain doubts like ... How we initialize weight . How we resize it .. why we reshape it and also .. so many doubts ... Also I want to know about the syntax for all those ... Finally what I exactly want is 😢I want to write a code or train a model. Without ai help... End of the day I want answer it with my own brain cells ... Could you plzz help . Me ....

arunawinslet.p

Bro, you said you upload data preprocessing video of house prediction dataset, please upload that soon, that will help us a lot

ares

This is a very under rated channel, needs more attention

shantanusingh

hi!, why did the vertical edge detector capture some of the slanting and horizontal edges?

arpit

You are a rely good teacher. Thank you. You show a very good and structural understanding of what you are talking about. May I ask in which university are you studying your Bachelor?

And, if I may couple of technical questions:
1)
When you apply "cubic filter" (one filter for each rgb channel) and refer filter vales as weights, are they different for each channel thus, if filter size is 3*3 *3 I have 27 different weights? Or there are actualy 9 weights same for each channel?
2)It is not an obvious idea to use weights from fully connected to affect previous step - act as filters in the cnn layers. I mean same values could benefit as filters and harm the result as being used as weights in the FC, and vice versa - they can be helpful as weights but harmfull as filter values... Why do they do it such a way?
3) would you agree to the next idea: flattening different channels values of image by a filter cube to one single value when there are more then 3 channels (say Infra-Red) doesnt sound a good idea, since there are times that IR has totaly unique information that none of the rgb channels has. What do you think?

igorg

I have one problem.
is every ML model has a neural network? If not please give me a summary of the ML Model with ANN & ML Model without ANN

miniwin

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